Numbers reported in text - para 3
# effect of ParC when added to GyrA-83 background (absence of any genes or GyrA-87)
# MIC data
QRDR_MIC_GyrA83background_noGenes_dat <- cipro_antibiogram %>%
filter(aac6==0 & acquired_genes==0 & `GyrA-83`==1 & `GyrA-87`==0) %>%
filter(grepl('MIC.*$', Laboratory.Typing.Method)) %>%
select(Measurement, Resistance.phenotype, resistant, nonWT_binary, ParC_mutations, `ParC-80`, `ParC-84`)
wilcox.test(log2(as.numeric(Measurement)) ~ ParC_mutations, data=QRDR_MIC_GyrA83background_noGenes_dat)
##
## Wilcoxon rank sum test with continuity correction
##
## data: log2(as.numeric(Measurement)) by ParC_mutations
## W = 13142, p-value = 0.001123
## alternative hypothesis: true location shift is not equal to 0
summary(lm(log2(as.numeric(Measurement)) ~ ParC_mutations, data=QRDR_MIC_GyrA83background_noGenes_dat))
##
## Call:
## lm(formula = log2(as.numeric(Measurement)) ~ ParC_mutations,
## data = QRDR_MIC_GyrA83background_noGenes_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.0946 -0.0946 -0.0946 0.2586 7.9054
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.7414 0.1394 5.320 1.43e-07 ***
## ParC_mutations 0.3532 0.1460 2.419 0.0158 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.061 on 648 degrees of freedom
## Multiple R-squared: 0.008949, Adjusted R-squared: 0.00742
## F-statistic: 5.852 on 1 and 648 DF, p-value: 0.01584
summary(as.numeric(QRDR_MIC_GyrA83background_noGenes_dat$Measurement)[QRDR_MIC_GyrA83background_noGenes_dat$`ParC_mutations`==1])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.25 2.00 2.00 4.69 2.00 512.00
summary(as.numeric(QRDR_MIC_GyrA83background_noGenes_dat$Measurement)[QRDR_MIC_GyrA83background_noGenes_dat$`ParC_mutations`==0])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.250 1.000 1.000 2.901 4.000 16.000
# disk diffusion
QRDR_DD_GyrA83background_noGenes_dat <- cipro_antibiogram %>%
filter(aac6==0 & acquired_genes==0 & `GyrA-83`==1 & `GyrA-87`==0) %>%
filter(Laboratory.Typing.Method=="Disk diffusion") %>%
select(Measurement, Resistance.phenotype, resistant, nonWT_binary, ParC_mutations, `ParC-80`, `ParC-84`)
wilcox.test(as.numeric(Measurement) ~ ParC_mutations, data=QRDR_DD_GyrA83background_noGenes_dat)
##
## Wilcoxon rank sum test with continuity correction
##
## data: as.numeric(Measurement) by ParC_mutations
## W = 1112.5, p-value = 5.475e-09
## alternative hypothesis: true location shift is not equal to 0
summary(lm(as.numeric(Measurement) ~ ParC_mutations, data=QRDR_DD_GyrA83background_noGenes_dat))
##
## Call:
## lm(formula = as.numeric(Measurement) ~ ParC_mutations, data = QRDR_DD_GyrA83background_noGenes_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.870 -3.585 -1.727 3.202 20.415
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.870 1.006 18.76 < 2e-16 ***
## ParC_mutations -9.285 1.204 -7.71 4.58e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.823 on 74 degrees of freedom
## Multiple R-squared: 0.4454, Adjusted R-squared: 0.4379
## F-statistic: 59.44 on 1 and 74 DF, p-value: 4.582e-11
summary(as.numeric(QRDR_DD_GyrA83background_noGenes_dat$Measurement)[QRDR_DD_GyrA83background_noGenes_dat$ParC_mutations==1])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 6.000 6.000 6.000 9.585 13.000 30.000
summary(as.numeric(QRDR_DD_GyrA83background_noGenes_dat$Measurement)[QRDR_DD_GyrA83background_noGenes_dat$ParC_mutations==0])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 11.00 16.00 20.00 18.87 22.00 25.00
# test categorial phenotypes
# all genomes with no acquired genes (MIC/DD)
QRDR_noGenes_dat <- cipro_antibiogram %>%
filter(aac6==0 & acquired_genes==0) %>%
select(Measurement, Resistance.phenotype, resistant, nonWT_binary, QRDR_mutations) %>%
mutate(qrdr_bin=if_else(QRDR_mutations==0, 0, 1))
# nonWT vs presence/absence
fisher.test(table(QRDR_noGenes_dat$nonWT_binary, QRDR_noGenes_dat$qrdr_bin))
##
## Fisher's Exact Test for Count Data
##
## data: table(QRDR_noGenes_dat$nonWT_binary, QRDR_noGenes_dat$qrdr_bin)
## p-value < 2.2e-16
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 525.1282 1264.3917
## sample estimates:
## odds ratio
## 826.9416
# resistance vs presence/absence
fisher.test(table(QRDR_noGenes_dat$resistant, QRDR_noGenes_dat$qrdr_bin))
##
## Fisher's Exact Test for Count Data
##
## data: table(QRDR_noGenes_dat$resistant, QRDR_noGenes_dat$qrdr_bin)
## p-value < 2.2e-16
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 1050.256 2419.794
## sample estimates:
## odds ratio
## 1521.631
Numbers reported in text - para 4
effect of presence/absence of any QRDR, in absence of any acquired
genes
# total strains without PMQR or aac6
nrow(QRDR_noGenes_dat)
## [1] 7457
# presence of QRDR vs MIC
QRDR_MIC_noGenes_dat <- cipro_antibiogram %>%
filter(grepl('MIC.*$', Laboratory.Typing.Method)) %>%
filter(aac6==0 & acquired_genes==0) %>%
select(Measurement, Resistance.phenotype, resistant, nonWT_binary, QRDR_mutations, `GyrA-83`, `ParC-80`) %>%
mutate(qrdr_bin=if_else(QRDR_mutations==0, 0, 1))
wilcox.test(log2(as.numeric(Measurement)) ~ qrdr_bin, data=QRDR_MIC_noGenes_dat)
##
## Wilcoxon rank sum test with continuity correction
##
## data: log2(as.numeric(Measurement)) by qrdr_bin
## W = 98162, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
summary(lm(log2(as.numeric(Measurement)) ~ qrdr_bin, data=QRDR_MIC_noGenes_dat))
##
## Call:
## lm(formula = log2(as.numeric(Measurement)) ~ qrdr_bin, data = QRDR_MIC_noGenes_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1830 -0.1830 -0.0414 -0.0414 7.9586
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.95856 0.01638 -119.6 <2e-16 ***
## qrdr_bin 3.14153 0.02793 112.5 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9207 on 4814 degrees of freedom
## Multiple R-squared: 0.7243, Adjusted R-squared: 0.7243
## F-statistic: 1.265e+04 on 1 and 4814 DF, p-value: < 2.2e-16
summary(as.numeric(QRDR_MIC_noGenes_dat$Measurement)[QRDR_MIC_noGenes_dat$qrdr_bin==1])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.250 2.000 2.000 5.536 2.000 512.000
summary(as.numeric(QRDR_MIC_noGenes_dat$Measurement)[QRDR_MIC_noGenes_dat$qrdr_bin==0])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0300 0.2500 0.2500 0.3421 0.2500 64.0000
# presence of QRDR vs DD zone
QRDR_DD_noGenes_dat <- cipro_antibiogram %>%
filter(aac6==0 & acquired_genes==0) %>%
filter(Laboratory.Typing.Method=="Disk diffusion") %>%
select(Measurement, Resistance.phenotype, resistant, nonWT_binary, QRDR_mutations) %>%
mutate(qrdr_bin=if_else(QRDR_mutations==0, 0, 1))
wilcox.test(as.numeric(Measurement) ~ qrdr_bin, data=QRDR_DD_noGenes_dat)
##
## Wilcoxon rank sum test with continuity correction
##
## data: as.numeric(Measurement) by qrdr_bin
## W = 320646, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
summary(lm(as.numeric(Measurement) ~ qrdr_bin, data=QRDR_DD_noGenes_dat))
##
## Call:
## lm(formula = as.numeric(Measurement) ~ qrdr_bin, data = QRDR_DD_noGenes_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -22.6659 -1.6659 0.3341 1.3341 18.1154
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 28.66587 0.06349 451.51 <2e-16 ***
## qrdr_bin -16.78125 0.28616 -58.64 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.181 on 2639 degrees of freedom
## Multiple R-squared: 0.5658, Adjusted R-squared: 0.5656
## F-statistic: 3439 on 1 and 2639 DF, p-value: < 2.2e-16
summary(as.numeric(QRDR_DD_noGenes_dat$Measurement)[QRDR_DD_noGenes_dat$qrdr_bin==1])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 6.00 6.00 9.00 11.88 17.00 30.00
summary(as.numeric(QRDR_DD_noGenes_dat$Measurement)[QRDR_DD_noGenes_dat$qrdr_bin==0])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 6.00 27.00 29.00 28.67 30.00 44.00
# presence of QRDR vs nonWT
fisher.test(table(QRDR_noGenes_dat$nonWT_binary, QRDR_noGenes_dat$qrdr_bin))
##
## Fisher's Exact Test for Count Data
##
## data: table(QRDR_noGenes_dat$nonWT_binary, QRDR_noGenes_dat$qrdr_bin)
## p-value < 2.2e-16
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 525.1282 1264.3917
## sample estimates:
## odds ratio
## 826.9416
# presence of QRDR vs R
fisher.test(table(QRDR_noGenes_dat$resistant, QRDR_noGenes_dat$qrdr_bin))
##
## Fisher's Exact Test for Count Data
##
## data: table(QRDR_noGenes_dat$resistant, QRDR_noGenes_dat$qrdr_bin)
## p-value < 2.2e-16
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 1050.256 2419.794
## sample estimates:
## odds ratio
## 1521.631
effect of number of QRDR mutations, in absence of any acquired
genes
# MIC vs QRDR count
QRDR_MIC_noGenes_dat <- QRDR_MIC_noGenes_dat %>%
mutate(QRDR_0_1_2 = if_else(QRDR_mutations>2, 2, QRDR_mutations)) %>%
mutate(QRDR_0_1_2_3 = if_else(QRDR_mutations>3, 3, QRDR_mutations)) %>%
mutate(QRDR_1_2 = if_else(QRDR_0_1_2==0, NA, QRDR_0_1_2)) %>%
mutate(QRDR_2_3 = if_else(QRDR_0_1_2<2, NA, QRDR_0_1_2_3))
# median MICs, grouped by QRDR count
summary(as.numeric(QRDR_MIC_noGenes_dat$Measurement)[QRDR_MIC_noGenes_dat$QRDR_mutations==0])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0300 0.2500 0.2500 0.3421 0.2500 64.0000
summary(as.numeric(QRDR_MIC_noGenes_dat$Measurement)[QRDR_MIC_noGenes_dat$QRDR_mutations==1])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.250 1.000 1.000 2.597 2.000 16.000
summary(as.numeric(QRDR_MIC_noGenes_dat$Measurement)[QRDR_MIC_noGenes_dat$QRDR_mutations>2])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.250 2.000 2.000 6.255 2.000 512.000
# test for difference in MIC with QRDR count
summary(lm(log2(as.numeric(Measurement)) ~ factor(QRDR_0_1_2), data=QRDR_MIC_noGenes_dat))
##
## Call:
## lm(formula = log2(as.numeric(Measurement)) ~ factor(QRDR_0_1_2),
## data = QRDR_MIC_noGenes_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2102 -0.2102 -0.0414 -0.0414 7.9586
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.95856 0.01633 -119.96 <2e-16 ***
## factor(QRDR_0_1_2)1 2.54190 0.10939 23.24 <2e-16 ***
## factor(QRDR_0_1_2)2 3.16879 0.02825 112.15 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9178 on 4813 degrees of freedom
## Multiple R-squared: 0.7262, Adjusted R-squared: 0.7261
## F-statistic: 6382 on 2 and 4813 DF, p-value: < 2.2e-16
summary(lm(log2(as.numeric(Measurement)) ~ QRDR_mutations, data=QRDR_MIC_noGenes_dat))
##
## Call:
## lm(formula = log2(as.numeric(Measurement)) ~ QRDR_mutations,
## data = QRDR_MIC_noGenes_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5764 -0.1041 -0.1041 -0.1041 8.5810
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.89590 0.01706 -111.1 <2e-16 ***
## QRDR_mutations 1.15743 0.01110 104.3 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9712 on 4814 degrees of freedom
## Multiple R-squared: 0.6933, Adjusted R-squared: 0.6932
## F-statistic: 1.088e+04 on 1 and 4814 DF, p-value: < 2.2e-16
# DD vs QRDR count
QRDR_DD_noGenes_dat <- QRDR_DD_noGenes_dat %>%
mutate(QRDR_0_1_2 = if_else(QRDR_mutations>2, 2, QRDR_mutations)) %>%
mutate(QRDR_0_1_2_3 = if_else(QRDR_mutations>3, 3, QRDR_mutations)) %>%
mutate(QRDR_1_2 = if_else(QRDR_0_1_2==0, NA, QRDR_0_1_2)) %>%
mutate(QRDR_2_3 = if_else(QRDR_0_1_2<2, NA, QRDR_0_1_2_3))
# median DD zones, grouped by QRDR count
summary(as.numeric(QRDR_DD_noGenes_dat$Measurement)[QRDR_DD_noGenes_dat$QRDR_mutations==0])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 6.00 27.00 29.00 28.67 30.00 44.00
summary(as.numeric(QRDR_DD_noGenes_dat$Measurement)[QRDR_DD_noGenes_dat$QRDR_mutations==1])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 11.00 17.00 20.50 19.75 22.25 29.00
summary(as.numeric(QRDR_DD_noGenes_dat$Measurement)[QRDR_DD_noGenes_dat$QRDR_mutations>2])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 6.000 6.000 6.000 6.676 6.000 12.000
# test for difference in DD zone with QRDR count
summary(lm(log2(as.numeric(Measurement)) ~ factor(QRDR_0_1_2), data=QRDR_DD_noGenes_dat))
##
## Call:
## lm(formula = log2(as.numeric(Measurement)) ~ factor(QRDR_0_1_2),
## data = QRDR_DD_noGenes_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.24805 -0.07812 0.02497 0.07388 1.96617
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.833011 0.003863 1251.15 <2e-16 ***
## factor(QRDR_0_1_2)1 -0.564658 0.030848 -18.30 <2e-16 ***
## factor(QRDR_0_1_2)2 -1.892292 0.020766 -91.12 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1936 on 2638 degrees of freedom
## Multiple R-squared: 0.7645, Adjusted R-squared: 0.7644
## F-statistic: 4283 on 2 and 2638 DF, p-value: < 2.2e-16
summary(lm(log2(as.numeric(Measurement)) ~ QRDR_mutations, data=QRDR_DD_noGenes_dat))
##
## Call:
## lm(formula = log2(as.numeric(Measurement)) ~ QRDR_mutations,
## data = QRDR_DD_noGenes_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.24831 -0.07839 0.02471 0.07362 1.57431
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.833275 0.003884 1244.46 <2e-16 ***
## QRDR_mutations -0.750349 0.008203 -91.47 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1953 on 2639 degrees of freedom
## Multiple R-squared: 0.7602, Adjusted R-squared: 0.7601
## F-statistic: 8367 on 1 and 2639 DF, p-value: < 2.2e-16
Fig1 - number of QRDR vs MIC, facet aac6 yes/no (no other
genes)
#MIC distribution for # QRDR, in absence of genes
QRDR_MIC_noGenes <- cipro_antibiogram %>%
filter(acquired_genes==0) %>%
filter(grepl('MIC.*$', Laboratory.Typing.Method)) %>%
mutate(aac6_label=if_else(aac6==0, "aac(6')-Ib-cr absent", "aac(6')-Ib-cr present")) %>%
mutate(QRDR_mutations=if_else(QRDR_mutations==4, 3, QRDR_mutations)) %>% # single isolate with 4 QRDR
ggplot(aes(x=factor(QRDR_mutations), y=as.numeric(Measurement))) +
geom_violin() +
geom_count(aes(colour = SRnwt)) +
geom_hline(aes(yintercept = 1), linetype = 2, alpha = 0.6, color = "black") +
geom_hline(aes(yintercept = 0.25), linetype = 1, alpha = 0.6, color = "black") +
facet_wrap(vars(aac6_label)) +
scale_y_continuous(trans = log2_trans(), breaks = 2^(-5:9), labels = function(x) round(as.numeric(x), digits = 3)) +
scale_color_manual(values = res_colours) +
theme_light() +
labs(y="MIC (mg/L)", x="", col="Phenotype",
title="a) No. QRDR mutations vs phenotype",
subtitle="(in absence of PMQR genes)")
QRDR_MIC_noGenes

QRDR_pheno_noGenes <- cipro_antibiogram %>%
filter(acquired_genes==0) %>%
filter(grepl('MIC.*$', Laboratory.Typing.Method)) %>%
mutate(aac6_label=if_else(aac6==0, "aac(6')-Ib-cr absent", "aac(6')-Ib-cr present")) %>%
mutate(QRDR_mutations=if_else(QRDR_mutations==4, 3, QRDR_mutations)) %>% # single isolate with 4 QRDR
ggplot(aes(x=factor(QRDR_mutations), fill=SRnwt)) +
geom_bar(stat='count', position='fill') +
facet_wrap(vars(aac6_label)) +
scale_fill_manual(values = res_colours) +
scale_y_continuous(labels=scales::percent_format()) +
geom_text(aes(label=..count..), stat="count", position=position_fill(vjust = .5), size=2) +
theme_light() +
labs(y="% Phenotype", x="Number of QRDR mutations") +
theme(legend.position="none", strip.background = element_blank(), strip.text = element_blank())
numbers for text - paragraph 5
# effect of qnr/qep genes, in absence of QRDR and aac6
# MIC data in absence of QRDR and aac6
qnr_MIC_nullBG_dat <- cipro_antibiogram %>%
filter(Laboratory.Typing.Method !="Disk diffusion") %>%
filter(aac6==0 & QRDR_mutations==0) %>%
select(Measurement, Resistance.phenotype, resistant, nonWT_binary, acquired_genes) %>%
mutate(acquired_bin=if_else(acquired_genes==0, 0, 1))
# MIC vs presence/absence of qnr, in absence of QRDR and aac6
summary(as.numeric(qnr_MIC_nullBG_dat$Measurement)[qnr_MIC_nullBG_dat$acquired_bin==1])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.125 0.500 1.000 1.575 2.000 8.000
summary(as.numeric(qnr_MIC_nullBG_dat$Measurement)[qnr_MIC_nullBG_dat$acquired_bin==0])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0300 0.2500 0.2500 0.3421 0.2500 64.0000
wilcox.test(log2(as.numeric(Measurement)) ~ acquired_bin, data=qnr_MIC_nullBG_dat)
##
## Wilcoxon rank sum test with continuity correction
##
## data: log2(as.numeric(Measurement)) by acquired_bin
## W = 140287, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
summary(lm(log2(as.numeric(Measurement)) ~ acquired_bin, data=qnr_MIC_nullBG_dat))
##
## Call:
## lm(formula = log2(as.numeric(Measurement)) ~ acquired_bin, data = qnr_MIC_nullBG_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1845 -0.0414 -0.0414 -0.0414 7.9586
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.95856 0.01615 -121.24 <2e-16 ***
## acquired_bin 2.14311 0.03952 54.23 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9081 on 3792 degrees of freedom
## Multiple R-squared: 0.4368, Adjusted R-squared: 0.4367
## F-statistic: 2941 on 1 and 3792 DF, p-value: < 2.2e-16
# DD data in absence of QRDR and aac6
qnr_DD_noGenes_dat <- cipro_antibiogram %>%
filter(aac6==0 & QRDR_mutations==0) %>%
filter(Laboratory.Typing.Method=="Disk diffusion") %>%
select(Measurement, Resistance.phenotype, resistant, nonWT_binary, acquired_genes) %>%
mutate(acquired_bin=if_else(acquired_genes==0, 0, 1))
# DD vs presence/absence of qnr, in absence of QRDR and aac6
summary(as.numeric(qnr_DD_noGenes_dat$Measurement)[qnr_DD_noGenes_dat$acquired_bin==1])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 6.00 17.00 21.00 19.51 22.00 31.00
summary(as.numeric(qnr_DD_noGenes_dat$Measurement)[qnr_DD_noGenes_dat$acquired_bin==0])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 6.00 27.00 29.00 28.67 30.00 44.00
wilcox.test(as.numeric(Measurement) ~ acquired_bin, data=qnr_DD_noGenes_dat)
##
## Wilcoxon rank sum test with continuity correction
##
## data: as.numeric(Measurement) by acquired_bin
## W = 230345, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
summary(lm(as.numeric(Measurement) ~ acquired_bin, data=qnr_DD_noGenes_dat))
##
## Call:
## lm(formula = as.numeric(Measurement) ~ acquired_bin, data = qnr_DD_noGenes_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -22.6659 -1.6659 0.3341 1.3341 15.3341
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 28.66587 0.05979 479.41 <2e-16 ***
## acquired_bin -9.15545 0.31160 -29.38 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.996 on 2605 degrees of freedom
## Multiple R-squared: 0.2489, Adjusted R-squared: 0.2486
## F-statistic: 863.3 on 1 and 2605 DF, p-value: < 2.2e-16
# all genomes with no acquired genes (MIC/DD)
qnr_noGenes_dat <- cipro_antibiogram %>%
filter(aac6==0 & QRDR_mutations==0) %>%
select(Measurement, Resistance.phenotype, resistant, nonWT_binary, acquired_genes) %>%
mutate(acquired_bin=if_else(acquired_genes==0, 0, 1))
dim(qnr_noGenes_dat)
## [1] 6401 6
# NWT vs presence/absence of qnr, in absence of QRDR and aac6
fisher.test(table(qnr_noGenes_dat$nonWT_binary, qnr_noGenes_dat$acquired_bin))
##
## Fisher's Exact Test for Count Data
##
## data: table(qnr_noGenes_dat$nonWT_binary, qnr_noGenes_dat$acquired_bin)
## p-value < 2.2e-16
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 86.53681 155.63478
## sample estimates:
## odds ratio
## 115.5569
# resistance vs presence/absence of qnr, in absence of QRDR and aac6
fisher.test(table(qnr_noGenes_dat$resistant, qnr_noGenes_dat$acquired_bin))
##
## Fisher's Exact Test for Count Data
##
## data: table(qnr_noGenes_dat$resistant, qnr_noGenes_dat$acquired_bin)
## p-value < 2.2e-16
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 74.21361 119.59424
## sample estimates:
## odds ratio
## 93.70919
# MIC vs qnr count
summary(lm(log2(as.numeric(Measurement)) ~ acquired_genes, data=qnr_MIC_nullBG_dat))
##
## Call:
## lm(formula = log2(as.numeric(Measurement)) ~ acquired_genes,
## data = qnr_MIC_nullBG_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1339 -0.0479 -0.0479 -0.0479 7.9521
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.95206 0.01614 -120.96 <2e-16 ***
## acquired_genes 2.04298 0.03781 54.03 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9096 on 3792 degrees of freedom
## Multiple R-squared: 0.435, Adjusted R-squared: 0.4348
## F-statistic: 2919 on 1 and 3792 DF, p-value: < 2.2e-16
summary(as.numeric(qnr_MIC_nullBG_dat$Measurement)[qnr_MIC_nullBG_dat$acquired_genes==1])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.125 0.500 1.000 1.546 2.000 8.000
summary(as.numeric(qnr_MIC_nullBG_dat$Measurement)[qnr_MIC_nullBG_dat$acquired_genes==2])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.5 1.0 2.0 2.5 4.0 4.0
summary(as.numeric(qnr_MIC_nullBG_dat$Measurement)[qnr_MIC_nullBG_dat$acquired_genes>2])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
##
# DD vs qnr count
summary(lm(as.numeric(Measurement) ~ acquired_genes, data=qnr_DD_noGenes_dat))
##
## Call:
## lm(formula = as.numeric(Measurement) ~ acquired_genes, data = qnr_DD_noGenes_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -22.6642 -1.6642 0.3358 1.3358 15.3358
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 28.66417 0.05939 482.65 <2e-16 ***
## acquired_genes -8.65839 0.28782 -30.08 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.978 on 2605 degrees of freedom
## Multiple R-squared: 0.2578, Adjusted R-squared: 0.2575
## F-statistic: 905 on 1 and 2605 DF, p-value: < 2.2e-16
summary(as.numeric(qnr_DD_noGenes_dat$Measurement)[qnr_DD_noGenes_dat$acquired_genes==1])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 6.00 18.00 21.00 19.91 22.50 31.00
summary(as.numeric(qnr_DD_noGenes_dat$Measurement)[qnr_DD_noGenes_dat$acquired_genes==2])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 6.0 6.0 10.0 12.2 17.0 22.0
summary(as.numeric(qnr_DD_noGenes_dat$Measurement)[qnr_DD_noGenes_dat$acquired_genes>2])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
##
numbers for text - paragraph 6, effect of aac6 gene, in absence of
QRDR and other acquired
# MIC
aac_MIC_nullBG_dat <- cipro_antibiogram %>%
filter(Laboratory.Typing.Method !="Disk diffusion") %>%
filter(acquired_genes==0 & QRDR_mutations==0) %>%
select(Measurement, Resistance.phenotype, resistant, nonWT_binary, aac6)
# MIC vs presence/absence of qnr
wilcox.test(log2(as.numeric(Measurement)) ~ aac6, data=aac_MIC_nullBG_dat)
##
## Wilcoxon rank sum test with continuity correction
##
## data: log2(as.numeric(Measurement)) by aac6
## W = 175387, p-value = 7.223e-14
## alternative hypothesis: true location shift is not equal to 0
summary(lm(log2(as.numeric(Measurement)) ~ aac6, data=aac_MIC_nullBG_dat))
##
## Call:
## lm(formula = log2(as.numeric(Measurement)) ~ aac6, data = aac_MIC_nullBG_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1003 -0.0414 -0.0414 -0.0414 7.9586
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.95856 0.01528 -128.145 <2e-16 ***
## aac6 0.62220 0.07203 8.639 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8592 on 3307 degrees of freedom
## Multiple R-squared: 0.02207, Adjusted R-squared: 0.02177
## F-statistic: 74.62 on 1 and 3307 DF, p-value: < 2.2e-16
summary(as.numeric(aac_MIC_nullBG_dat$Measurement)[aac_MIC_nullBG_dat$aac6==1])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.1200 0.2500 0.2500 0.6249 0.5000 4.0000
summary(as.numeric(aac_MIC_nullBG_dat$Measurement)[aac_MIC_nullBG_dat$aac6==0])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0300 0.2500 0.2500 0.3421 0.2500 64.0000
aac_MIC_nullBG_dat %>% group_by(aac6) %>% summarise(S=mean(Resistance.phenotype=="S"), I=mean(Resistance.phenotype=="I"), R=mean(Resistance.phenotype=="R"))
# DD
aac_DD_nullBG_dat <- cipro_antibiogram %>%
filter(acquired_genes==0 & QRDR_mutations==0) %>%
filter(Laboratory.Typing.Method=="Disk diffusion") %>%
select(Measurement, Resistance.phenotype, resistant, nonWT_binary, aac6)
wilcox.test(as.numeric(Measurement) ~ aac6, data=aac_DD_nullBG_dat)
##
## Wilcoxon rank sum test with continuity correction
##
## data: as.numeric(Measurement) by aac6
## W = 25156, p-value = 5.356e-05
## alternative hypothesis: true location shift is not equal to 0
summary(lm(as.numeric(Measurement) ~ aac6, data=aac_DD_nullBG_dat))
##
## Call:
## lm(formula = as.numeric(Measurement) ~ aac6, data = aac_DD_nullBG_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -22.6659 -1.6659 0.3341 1.3341 15.3341
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 28.66587 0.05766 497.182 < 2e-16 ***
## aac6 -5.08254 0.83602 -6.079 1.39e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.889 on 2521 degrees of freedom
## Multiple R-squared: 0.01445, Adjusted R-squared: 0.01406
## F-statistic: 36.96 on 1 and 2521 DF, p-value: 1.389e-09
summary(as.numeric(aac_DD_nullBG_dat$Measurement)[aac_DD_nullBG_dat$aac6==1])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 16.00 21.00 23.50 23.58 27.25 29.00
summary(as.numeric(aac_DD_nullBG_dat$Measurement)[aac_DD_nullBG_dat$aac6==0])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 6.00 27.00 29.00 28.67 30.00 44.00
aac_DD_nullBG_dat %>% group_by(aac6) %>% summarise(S=mean(Resistance.phenotype=="S"), I=mean(Resistance.phenotype=="I"), R=mean(Resistance.phenotype=="R"))
# all genomes with no acquired genes (MIC/DD)
qnr_noGenes_dat <- cipro_antibiogram %>%
filter(acquired_genes==0 & QRDR_mutations==0) %>%
select(Measurement, Resistance.phenotype, resistant, nonWT_binary, aac6)
dim(qnr_noGenes_dat)
## [1] 5832 5